A Ouahabi
Signal and Image Multiresolution Analysis
Gebonden Engels 2012 9781848212572
Verwachte levertijd ongeveer 9 werkdagen
Samenvatting
Multiresolution analysis using the wavelet transform has received considerable attention in recent years by researchers in various fields. It is a powerful tool for efficiently representing signals and images at multiple levels of detail with many inherent advantages, including compression, level–of–detail display, progressive transmission, level–of–detail editing, filtering, modeling, fractals and multifractals, etc.
This book aims to provide a simple formalization and new clarity on multiresolution analysis, rendering accessible obscure techniques, and merging, unifying or completing the technique with encoding, feature extraction, compressive sensing, multifractal analysis and texture analysis. It is aimed at industrial engineers, medical researchers, university lab attendants, lecturer–researchers and researchers from various specializations. It is also intended to contribute to the studies of graduate students in engineering, particularly in the fields of medical imaging, intelligent instrumentation, telecommunications, and signal and image processing.
Given the diversity of the problems posed and addressed, this book paves the way for the development of new research themes, such as brain computer interface (BCI), compressive sensing, functional magnetic resonance imaging (fMRI), tissue characterization (bones, skin, etc.) and the analysis of complex phenomena in general. Throughout the chapters, informative illustrations assist the uninitiated reader in better conceptualizing certain concepts, taking the form of numerous figures and recent applications in biomedical engineering, communication, multimedia, finance, etc.
Specificaties
ISBN13:9781848212572
Taal:Engels
Bindwijze:gebonden
Aantal pagina's:320
Uitgever:John Wiley & Sons
Serie:ISTE
Lezersrecensies
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Inhoudsopgave
<p>Introduction xi</p>
<p>Chapter 1. Introduction to Multiresolution Analysis 1</p>
<p>1.1. Introduction 1</p>
<p>1.2. Wavelet transforms: an introductory review 3</p>
<p>1.2.1. Brief history 3</p>
<p>1.2.2. Continuous wavelet transforms 6</p>
<p>1.2.2.1. Wavelet transform modulus maxima 9</p>
<p>1.2.2.2. Reconstruction 13</p>
<p>1.2.3. Discrete wavelet transforms 14</p>
<p>1.3. Multiresolution 16</p>
<p>1.3.1. Multiresolution analysis and wavelet bases 17</p>
<p>1.3.1.1. Approximation spaces 17</p>
<p>1.3.1.2. Detail spaces 19</p>
<p>1.3.2. Multiresolution analysis: points to remember 21</p>
<p>1.3.3. Decomposition and reconstruction 22</p>
<p>1.3.3.1. Calculation of coefficients 22</p>
<p>1.3.3.2. Implementation of MRA: Mallat algorithm 24</p>
<p>1.3.3.3. Extension to images 26</p>
<p>1.3.4. Wavelet packets 28</p>
<p>1.3.5. Multiresolution analysis summarized 30</p>
<p>1.4. Which wavelets to choose? 33</p>
<p>1.4.1. Number of vanishing moments, regularity,</p>
<p>support (compactness), symmetry, etc 33</p>
<p>1.4.2. Well–known wavelets, scale functions and associated filters 34</p>
<p>1.4.2.1. Haar wavelet 34</p>
<p>vi Signal and Image Multiresolution Analysis</p>
<p>1.4.2.2. Daubechies wavelets 36</p>
<p>1.4.2.3. Symlets 38</p>
<p>1.4.2.4. Coiflets 39</p>
<p>1.4.2.5. Meyer wavelets 41</p>
<p>1.4.2.6. Polynomial spline wavelets 43</p>
<p>1.5. Multiresolution analysis and biorthogonal wavelet bases 48</p>
<p>1.5.1. Why biorthogonal bases? 48</p>
<p>1.5.2. Multiresolution context 48</p>
<p>1.5.3. Example of biorthogonal wavelets, scaling functions</p>
<p>and associated filters 49</p>
<p>1.5.4. The concept of wavelet lifting 51</p>
<p>1.5.4.1. The notion of lifting 51</p>
<p>1.5.4.2. Significance of structure lifting 52</p>
<p>1.6. Wavelet choice at a glance 54</p>
<p>1.6.1. Regularity 54</p>
<p>1.6.2. Vanishing moments 54</p>
<p>1.6.3. Other criteria 55</p>
<p>1.6.4. Conclusion 55</p>
<p>1.7. Worked examples 55</p>
<p>1.7.1. Examples of multiresolution analysis 55</p>
<p>1.7.2. Compression 58</p>
<p>1.7.3. Denoising (reduction of noise) 64</p>
<p>1.8. Some applications 74</p>
<p>1.8.1. Discovery and contributions of wavelets 74</p>
<p>1.8.2. Biomedical engineering 76</p>
<p>1.8.2.1. ECG, EEG and BCI 77</p>
<p>1.8.2.2. Medical imaging 97</p>
<p>1.8.3. Telecommunications 110</p>
<p>1.8.3.1. Adaptive compression for sensor networks 110</p>
<p>1.8.3.2. Masking image encoding and transmission errors 114</p>
<p>1.8.3.3. Suppression of correlated noise 118</p>
<p>1.8.4. Compressive sensing , ICA, PCA and MRA 119</p>
<p>1.8.4.1. Principal component analysis 120</p>
<p>1.8.4.2. Independent component analysis 121</p>
<p>1.8.4.3. Compressive sensing 122</p>
<p>1.8.5. Conclusion 128</p>
<p>1.9. Bibliography 129</p>
<p>Chapter 2. Discrete Wavelet Transform–Based Multifractal Analysis 135</p>
<p>2.1. Introduction 135</p>
<p>2.1.1. Fractals and wavelets: a happy marriage? 135</p>
<p>2.1.2. Background 136</p>
<p>2.1.3. Mono/multifractal processes 137</p>
<p>2.1.4. Chapter outline 138</p>
<p>2.2. Fractality, variability and complexity 139</p>
<p>2.2.1. System complexity 139</p>
<p>2.2.2. Complex phenomena properties 141</p>
<p>2.2.2.1. Tendency of autonomous agents to self–organize 141</p>
<p>2.2.2.2. Variability and adaptability 142</p>
<p>2.2.2.3. Bifurcation concept and chaotic model 143</p>
<p>2.2.2.4. Hierarchy and scale invariance 146</p>
<p>2.2.2.5. Self–organized critical phenomena 146</p>
<p>2.2.2.6. Highly optimized tolerance 147</p>
<p>2.2.3. Fractality 148</p>
<p>2.3. Multifractal analysis 150</p>
<p>2.3.1. Point–wise regularity 150</p>
<p>2.3.2. Hölder exponent 150</p>
<p>2.3.3. Signal classification according to the regularity properties 152</p>
<p>2.3.3.1. Monofractal signal 152</p>
<p>2.3.3.2. Multifractal signal 152</p>
<p>2.3.4. Hausdorff dimension 154</p>
<p>2.3.4.1. Theoretic approach 155</p>
<p>2.3.4.2. Qualitative approach and multifractal spectrum 155</p>
<p>2.4. Multifractal formalism 156</p>
<p>2.4.1. Reminder on wavelet decomposition 156</p>
<p>2.4.2. Point–wise regularity characterization 157</p>
<p>2.4.3. Structure function and power law behavior 158</p>
<p>2.4.4. Link between scaling exponents and singularity spectrum 159</p>
<p>2.4.5. Use of wavelet leaders 160</p>
<p>2.4.5.1. Indexing a dyadic square and wavelet leaders 161</p>
<p>2.4.5.2. Polynomial expansion and log–cumulants 162</p>
<p>2.4.6. Wavelet leaders variant: maximum coefficients 165</p>
<p>2.5. Algorithm and performances 165</p>
<p>2.5.1. Singularity spectrum estimation algorithm 165</p>
<p>2.5.2. Analysis of a few widely used processes 167</p>
<p>2.5.2.1. fBm: a monofractal process 167</p>
<p>2.5.2.2. CMC: a multifractal process 170</p>
<p>2.5.2.3. BMC: another class of multifractal processes 173</p>
<p>2.5.3. Estimation performances 176</p>
<p>2.5.3.1. fBm and CMC–LN and CMC–LP simulation 176</p>
<p>2.5.3.2. Results 177</p>
<p>2.5.3.3. Interpretation and recommendations 182</p>
<p>2.6. Applications 186</p>
<p>2.6.1. Turbulence 186</p>
<p>viii Signal and Image Multiresolution Analysis</p>
<p>2.6.1.1. From Leonardo da Vinci to Kolmogorov 186</p>
<p>2.6.1.2. Multifractal process in turbulence 191</p>
<p>2.6.2. Multifractal process in finance 194</p>
<p>2.6.2.1. Stock market complexity modeling 194</p>
<p>2.6.2.2. Market turbulence 198</p>
<p>2.6.3. Internet traffic 203</p>
<p>2.6.3.1. The Internet revolution 203</p>
<p>2.6.3.2. Multifractal nature of Internet traffic? 204</p>
<p>2.6.4. Biomedical field 205</p>
<p>2.6.4.1. Analyzing image texture through a multifractal approach 205</p>
<p>2.6.4.2. Multifractality in medical imaging 211</p>
<p>2.7. Conclusion 219</p>
<p>2.8. Bibliography 220</p>
<p>Chapter 3. Multimodal Compression Using JPEG 2000:</p>
<p>Supervised Insertion Approach 225</p>
<p>3.1. Introduction 225</p>
<p>3.2. The JPEG 2000 standard 226</p>
<p>3.3. Multimodal compression by unsupervised insertion 227</p>
<p>3.3.1. Principle of insertion in the wavelet transform domain 228</p>
<p>3.3.2. Principle of insertion in the spatial domain 229</p>
<p>3.4. Multimodal compression by supervised insertion 231</p>
<p>3.4.1. Choice of insertion zone 232</p>
<p>3.4.2. Insertion and separation function 233</p>
<p>3.4.2.1. Insertion function 233</p>
<p>3.4.2.2. Separation function 235</p>
<p>3.5. Criteria for quality evaluation 236</p>
<p>3.5.1. Peak signal–to–noise ratio 236</p>
<p>3.5.2. Percent residual difference 237</p>
<p>3.6. Some preliminary results 238</p>
<p>3.7. Conclusion 242</p>
<p>3.8. Bibliography 243</p>
<p>Chapter 4. Cerebral Microembolism Synchronous Detection</p>
<p>with Wavelet Packets 245</p>
<p>4.1. Issue and stakes 245</p>
<p>4.2. Prior information research 247</p>
<p>4.2.1. Doppler ultrasound blood flow signal 247</p>
<p>4.2.2. Embolic Doppler ultrasound signal 250</p>
<p>4.3. Doppler ultrasound blood emboli signal modeling 251</p>
<p>4.3.1. Physical model 251</p>
<p>4.3.2. Signal model 255</p>
<p>4.3.3. Statistical tests 258</p>
<p>4.3.3.1. Stationarity test 259</p>
<p>4.3.3.2. Cyclostationarity test 261</p>
<p>4.3.3.3. Gaussian test and cardiac cycle regularity 262</p>
<p>4.4. Energy detection 263</p>
<p>4.4.1. State–of–the–art and standard detection 263</p>
<p>4.4.2. Synchronous detection 266</p>
<p>4.5. Wavelet packet energy detection 269</p>
<p>4.5.1. Introduction 269</p>
<p>4.5.2. Multiresolution analysis 271</p>
<p>4.5.3. Wavelet packet subband detection 274</p>
<p>4.5.4. Wavelet packet synchronous detection 277</p>
<p>4.6. Results and discussions 279</p>
<p>4.6.1. In simulation 279</p>
<p>4.6.2. In vivo 282</p>
<p>4.7. Conclusion 285</p>
<p>4.8. Bibliography 285</p>
<p>List of Authors 289</p>
<p>Index 291</p>
<p>Chapter 1. Introduction to Multiresolution Analysis 1</p>
<p>1.1. Introduction 1</p>
<p>1.2. Wavelet transforms: an introductory review 3</p>
<p>1.2.1. Brief history 3</p>
<p>1.2.2. Continuous wavelet transforms 6</p>
<p>1.2.2.1. Wavelet transform modulus maxima 9</p>
<p>1.2.2.2. Reconstruction 13</p>
<p>1.2.3. Discrete wavelet transforms 14</p>
<p>1.3. Multiresolution 16</p>
<p>1.3.1. Multiresolution analysis and wavelet bases 17</p>
<p>1.3.1.1. Approximation spaces 17</p>
<p>1.3.1.2. Detail spaces 19</p>
<p>1.3.2. Multiresolution analysis: points to remember 21</p>
<p>1.3.3. Decomposition and reconstruction 22</p>
<p>1.3.3.1. Calculation of coefficients 22</p>
<p>1.3.3.2. Implementation of MRA: Mallat algorithm 24</p>
<p>1.3.3.3. Extension to images 26</p>
<p>1.3.4. Wavelet packets 28</p>
<p>1.3.5. Multiresolution analysis summarized 30</p>
<p>1.4. Which wavelets to choose? 33</p>
<p>1.4.1. Number of vanishing moments, regularity,</p>
<p>support (compactness), symmetry, etc 33</p>
<p>1.4.2. Well–known wavelets, scale functions and associated filters 34</p>
<p>1.4.2.1. Haar wavelet 34</p>
<p>vi Signal and Image Multiresolution Analysis</p>
<p>1.4.2.2. Daubechies wavelets 36</p>
<p>1.4.2.3. Symlets 38</p>
<p>1.4.2.4. Coiflets 39</p>
<p>1.4.2.5. Meyer wavelets 41</p>
<p>1.4.2.6. Polynomial spline wavelets 43</p>
<p>1.5. Multiresolution analysis and biorthogonal wavelet bases 48</p>
<p>1.5.1. Why biorthogonal bases? 48</p>
<p>1.5.2. Multiresolution context 48</p>
<p>1.5.3. Example of biorthogonal wavelets, scaling functions</p>
<p>and associated filters 49</p>
<p>1.5.4. The concept of wavelet lifting 51</p>
<p>1.5.4.1. The notion of lifting 51</p>
<p>1.5.4.2. Significance of structure lifting 52</p>
<p>1.6. Wavelet choice at a glance 54</p>
<p>1.6.1. Regularity 54</p>
<p>1.6.2. Vanishing moments 54</p>
<p>1.6.3. Other criteria 55</p>
<p>1.6.4. Conclusion 55</p>
<p>1.7. Worked examples 55</p>
<p>1.7.1. Examples of multiresolution analysis 55</p>
<p>1.7.2. Compression 58</p>
<p>1.7.3. Denoising (reduction of noise) 64</p>
<p>1.8. Some applications 74</p>
<p>1.8.1. Discovery and contributions of wavelets 74</p>
<p>1.8.2. Biomedical engineering 76</p>
<p>1.8.2.1. ECG, EEG and BCI 77</p>
<p>1.8.2.2. Medical imaging 97</p>
<p>1.8.3. Telecommunications 110</p>
<p>1.8.3.1. Adaptive compression for sensor networks 110</p>
<p>1.8.3.2. Masking image encoding and transmission errors 114</p>
<p>1.8.3.3. Suppression of correlated noise 118</p>
<p>1.8.4. Compressive sensing , ICA, PCA and MRA 119</p>
<p>1.8.4.1. Principal component analysis 120</p>
<p>1.8.4.2. Independent component analysis 121</p>
<p>1.8.4.3. Compressive sensing 122</p>
<p>1.8.5. Conclusion 128</p>
<p>1.9. Bibliography 129</p>
<p>Chapter 2. Discrete Wavelet Transform–Based Multifractal Analysis 135</p>
<p>2.1. Introduction 135</p>
<p>2.1.1. Fractals and wavelets: a happy marriage? 135</p>
<p>2.1.2. Background 136</p>
<p>2.1.3. Mono/multifractal processes 137</p>
<p>2.1.4. Chapter outline 138</p>
<p>2.2. Fractality, variability and complexity 139</p>
<p>2.2.1. System complexity 139</p>
<p>2.2.2. Complex phenomena properties 141</p>
<p>2.2.2.1. Tendency of autonomous agents to self–organize 141</p>
<p>2.2.2.2. Variability and adaptability 142</p>
<p>2.2.2.3. Bifurcation concept and chaotic model 143</p>
<p>2.2.2.4. Hierarchy and scale invariance 146</p>
<p>2.2.2.5. Self–organized critical phenomena 146</p>
<p>2.2.2.6. Highly optimized tolerance 147</p>
<p>2.2.3. Fractality 148</p>
<p>2.3. Multifractal analysis 150</p>
<p>2.3.1. Point–wise regularity 150</p>
<p>2.3.2. Hölder exponent 150</p>
<p>2.3.3. Signal classification according to the regularity properties 152</p>
<p>2.3.3.1. Monofractal signal 152</p>
<p>2.3.3.2. Multifractal signal 152</p>
<p>2.3.4. Hausdorff dimension 154</p>
<p>2.3.4.1. Theoretic approach 155</p>
<p>2.3.4.2. Qualitative approach and multifractal spectrum 155</p>
<p>2.4. Multifractal formalism 156</p>
<p>2.4.1. Reminder on wavelet decomposition 156</p>
<p>2.4.2. Point–wise regularity characterization 157</p>
<p>2.4.3. Structure function and power law behavior 158</p>
<p>2.4.4. Link between scaling exponents and singularity spectrum 159</p>
<p>2.4.5. Use of wavelet leaders 160</p>
<p>2.4.5.1. Indexing a dyadic square and wavelet leaders 161</p>
<p>2.4.5.2. Polynomial expansion and log–cumulants 162</p>
<p>2.4.6. Wavelet leaders variant: maximum coefficients 165</p>
<p>2.5. Algorithm and performances 165</p>
<p>2.5.1. Singularity spectrum estimation algorithm 165</p>
<p>2.5.2. Analysis of a few widely used processes 167</p>
<p>2.5.2.1. fBm: a monofractal process 167</p>
<p>2.5.2.2. CMC: a multifractal process 170</p>
<p>2.5.2.3. BMC: another class of multifractal processes 173</p>
<p>2.5.3. Estimation performances 176</p>
<p>2.5.3.1. fBm and CMC–LN and CMC–LP simulation 176</p>
<p>2.5.3.2. Results 177</p>
<p>2.5.3.3. Interpretation and recommendations 182</p>
<p>2.6. Applications 186</p>
<p>2.6.1. Turbulence 186</p>
<p>viii Signal and Image Multiresolution Analysis</p>
<p>2.6.1.1. From Leonardo da Vinci to Kolmogorov 186</p>
<p>2.6.1.2. Multifractal process in turbulence 191</p>
<p>2.6.2. Multifractal process in finance 194</p>
<p>2.6.2.1. Stock market complexity modeling 194</p>
<p>2.6.2.2. Market turbulence 198</p>
<p>2.6.3. Internet traffic 203</p>
<p>2.6.3.1. The Internet revolution 203</p>
<p>2.6.3.2. Multifractal nature of Internet traffic? 204</p>
<p>2.6.4. Biomedical field 205</p>
<p>2.6.4.1. Analyzing image texture through a multifractal approach 205</p>
<p>2.6.4.2. Multifractality in medical imaging 211</p>
<p>2.7. Conclusion 219</p>
<p>2.8. Bibliography 220</p>
<p>Chapter 3. Multimodal Compression Using JPEG 2000:</p>
<p>Supervised Insertion Approach 225</p>
<p>3.1. Introduction 225</p>
<p>3.2. The JPEG 2000 standard 226</p>
<p>3.3. Multimodal compression by unsupervised insertion 227</p>
<p>3.3.1. Principle of insertion in the wavelet transform domain 228</p>
<p>3.3.2. Principle of insertion in the spatial domain 229</p>
<p>3.4. Multimodal compression by supervised insertion 231</p>
<p>3.4.1. Choice of insertion zone 232</p>
<p>3.4.2. Insertion and separation function 233</p>
<p>3.4.2.1. Insertion function 233</p>
<p>3.4.2.2. Separation function 235</p>
<p>3.5. Criteria for quality evaluation 236</p>
<p>3.5.1. Peak signal–to–noise ratio 236</p>
<p>3.5.2. Percent residual difference 237</p>
<p>3.6. Some preliminary results 238</p>
<p>3.7. Conclusion 242</p>
<p>3.8. Bibliography 243</p>
<p>Chapter 4. Cerebral Microembolism Synchronous Detection</p>
<p>with Wavelet Packets 245</p>
<p>4.1. Issue and stakes 245</p>
<p>4.2. Prior information research 247</p>
<p>4.2.1. Doppler ultrasound blood flow signal 247</p>
<p>4.2.2. Embolic Doppler ultrasound signal 250</p>
<p>4.3. Doppler ultrasound blood emboli signal modeling 251</p>
<p>4.3.1. Physical model 251</p>
<p>4.3.2. Signal model 255</p>
<p>4.3.3. Statistical tests 258</p>
<p>4.3.3.1. Stationarity test 259</p>
<p>4.3.3.2. Cyclostationarity test 261</p>
<p>4.3.3.3. Gaussian test and cardiac cycle regularity 262</p>
<p>4.4. Energy detection 263</p>
<p>4.4.1. State–of–the–art and standard detection 263</p>
<p>4.4.2. Synchronous detection 266</p>
<p>4.5. Wavelet packet energy detection 269</p>
<p>4.5.1. Introduction 269</p>
<p>4.5.2. Multiresolution analysis 271</p>
<p>4.5.3. Wavelet packet subband detection 274</p>
<p>4.5.4. Wavelet packet synchronous detection 277</p>
<p>4.6. Results and discussions 279</p>
<p>4.6.1. In simulation 279</p>
<p>4.6.2. In vivo 282</p>
<p>4.7. Conclusion 285</p>
<p>4.8. Bibliography 285</p>
<p>List of Authors 289</p>
<p>Index 291</p>
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